The Importance of Ukrainian Developers for Global Success

Developing a web or mobile app gives a business great opportunities and provides benefits in the form of additional dividends and audience loyalty. Having a clear understanding of the end goals, it is essential to define the main functionality of the future application to achieve the business goals and effectively perform setting tasks.

How to identify core functionalities for your web/mobile application?

Before developing an application, businesses must understand what features should be included in the finished product. The efficiency of the application itself and the speed of achieving the set KPIs depend on this. What may work for one company does not resonate with the business goals of other companies. In this list, we cover what you should consider when compiling a feature list:

1. Define your business goals 

What goals do you want to achieve with the app? Make a clear list of the problems your app will solve, what target audience will use it, and what preliminary results you expect.

2. Research the market and competitors

Analyze the market in your niche to understand what functionality your competitors already offer. Research and analyze their applications to understand what advantages they offer and disadvantages their products have. That will help you determine relevant features for your app to stand out in the market.

3. Make a preliminary list of features

Before development, list those features that will help you achieve your goals. Divide them into core and optional features. In the core features, list the ones without which your app won't be able to function efficiently and solve the users' problem. In the optional ones, list the ones which are not essential to the basic functionality but can add value and competitive advantage.

4. Prioritize features

Determine which features in the application are most important to achieve your initial goal. Prioritize and determine which features can be added later in later app versions. That will help you focus on developing the more essential components. 

5. Conduct user research

The research will help you understand the needs and preferences of your target audience. Conduct surveys, interviews, focus groups, or analysis of user behavior in your niche. Use this data to refine and tailor your feature list to your users' needs.

6. Create user scenarios

Develop different user scenarios to better understand how users will interact with your app and what features they need to achieve their goals. For example, define how users will sign in, log in, perform specific tasks, make purchases, or interact with other users.

Top-9 Core functionalities for every web/mobile application

Multi-Device Synchronization

It is a process in which different devices work together and exchange information so that the data and settings on all devices are the same or consistent.

When devices synchronize, they usually use the Internet to exchange data. For example, you might sync your smartphone with your laptop to update contacts, calendars, and photos on both devices. When you make changes on one device, those changes are automatically transferred to other devices so that all appliances have up-to-date information. 

This makes working on multiple devices as easy as possible, gives you a significant advantage in the app market, and guarantees more users.‍

A User-Friendly and Responsive Interface

In addition to a functional set, it is vital to think about a pleasant design and clear navigation for the user's enjoyable and convenient use of the application. In this way, interaction with the application is easy and effortless. 

The user-friendly interface has clear and easily accessible buttons and menus, well-organized information, and clear instructions. This allows you to perform the necessary tasks without unnecessary complications.

The responsive interface ensures that the system responds quickly to user actions. When you press a button or perform an action, the responsive interface responds instantly and without delay. Or when you scroll through a web page or type on the keyboard, the responsive interface displays changes instantly and smoothly, with no visible delays.

The success of an application is directly related to its loading speed and the attractiveness of the interface. This is what users look for when choosing a user-friendly app.

A Search Option

This feature allows you to find the information you want in the app or on a website. When you enter keywords or phrases related to what the user is looking for, the app scans its database or content to find relevant results.

The search option saves time and effort by helping to search for specific items or information the user is looking for instead of having to browse everything manually. This option is handy on significant marketplaces to search for a particular item.

Push notifications

Push notifications typically appear on the screen of any user's device in the form of small messages or icons, including text, images, or sounds. They are designed to get your attention and notify you of updates or social media activity.

They are convenient and essential because they help you stay informed and ensure you get all the important information. However, if the user receives too many notifications, they can become intrusive or distracting. Therefore, it is crucial to provide the ability to customize their settings, which the user will set independently according to their preferences.

Offline Capabilities

With offline access, users can browse the app's content even when the device is not connected to Wi-Fi or a mobile network. For example, you can view and edit documents, listen to music, or watch videos without the Internet.

It is often the case that you cannot connect to the Internet or have no network, for example, when traveling in regions with poor coverage or when saving data traffic. Therefore, this feature will be a great advantage for those users who want to use the application offline.

Analytics Tools

Analytics tools in a web or mobile app can track various user actions, such as app launches, page views, button clicks, interaction with interface elements, and other events.

Using this data, you can make informed decisions, make changes, optimize your app, and improve its functionality to meet user needs and increase the effectiveness of your app.

Security

Security refers to the measures and mechanisms built into the application to protect data and keep users safe. This process includes data encryption, protection against unauthorized access, and user information storage in secure locations.

This is a basic but important feature that will not only keep each user's data secure and protected but will also increase trust and loyalty to your app.

Online Payments

Online payments allow you to purchase in online stores, pay bills for services, send money to others, and much more. It is convenient because the user can make payments at any time and from any place with Internet access. To do this, it is necessary to connect an electronic payment system through which the payment will take place. 

Due to the growing number of online stores, stores, and food delivery companies—the need to implement this function is increasing.

Integrations

Integration refers to the creation of links and interactions between different software components. This allows them to exchange data, transfer information, and use each other's functionality. 

For example, suppose you have a hotel booking site. In that case, you can integrate it with maps and geolocation systems to offer users the nearest available hotels and show them on a map. Integrating various features and services into web and mobile apps makes the user experience easier and better. 

Integrations help create more useful and powerful apps, extend functionality, and enable easy data exchange with other systems or services.

Summary

Mobile and web apps allow users to access information, interact with others, shop, automate workflows, simplify daily activities, and more through a user—friendly interface and unique features. 

Which features you choose depends on your business, its needs, and the app's purpose. Everything is tailored individually. Combining all of the above features can be the perfect modern app to meet the needs of your customers. But if, for some reason, one of the items doesn't work for you, use something that will definitely increase the efficiency of your product.

In today's world of IT technologies, cooperation with Ukrainian developers is becoming more and more relevant and promising. Ukraine is ranked as one of the world's top IT service providers and continues to hold that position.

Ukrainian IT market overview

Today, the Ukrainian IT market has positive growth dynamics, as Ukraine is a more favorable center of cooperation than other countries. Why is this so? It is due to our specialists' high level of education, scientific potential, the country's proximity to the European market, and favorable tax conditions. The close geographical location to Europe also ensures faster and more efficient communication between clients and developers. All this makes it possible to double Ukraine's export revenues in the next 4 years.

Currently, more than 200,000 Ukrainian developers work in the international market. That demonstrates the high level of qualification and professionalism of IT specialists who successfully work on projects of any scale and complexity.

In 2021-2022, the IT industry grew by 36%, from $5 billion to $6.8 billion in exports. Over the past three years, these figures have almost doubled, and the growth trend is quite stable. Despite the war and issues many IT companies face nowadays, the IT sector is still a driving force in the Ukrainian economy and continues to develop. 

The majority of IT companies today work remotely. But this does not prevent participants in the development process from creating digital products, being in touch with the client, and providing timely updates on the work done. During the full-scale invasion of the aggressor country, all Ukrainian companies have adapted to the new reality and are ready for force-majeure situations at any level. 

Our company did not disrupt any project over the past year and delivered all developed products to each customer. Moreover, our practical work resulted in the support of foreign customers who want to cooperate only with Ukrainian companies on convenient terms.

Development tasks that Ukrainian developers solve

Ukrainian developers today are among the most in-demand in the world. Through continuous development and training, top-notch IT specialists contribute to developing international products and participate in their launch and maintenance. They successfully solve many development tasks and problems, including:

1. Complex technical projects

‍Ukrainian developers have high technical expertise and can implement projects of any complexity. They use the most advanced technologies and programming languages, such as Java, Python, Ruby, JavaScript, etc.

2. Mobile application development

A significant amount of experience is available among Ukrainian developers in developing mobile applications for iOS and Android platforms using React Native, Ionic, and other technologies. They know how to create intuitive, user-friendly interfaces that satisfy users' needs. They also specialize in developing cross-platform applications that run on multiple operating systems, which can significantly speed up the development process and reduce project costs. 

3. E-commerce platform development 

Ukrainian developers also have considerable experience in developing online stores and e-commerce platforms. They are familiar with major CMS systems such as Magento, Shopify, and WooCommerce.

4. Software development 

It is well known that Ukrainian developers create high-quality software. They know how to write code that will work quickly, efficiently, and reliably. They also have experience in various areas, such as web development, mobile development, artificial intelligence, and more. Ukrainian developers often use modern technologies and development methods such as Agile, Scrum, and DevOps to ensure high quality and fast development time.

5. Integration and maintenance

Besides developing new software, Ukrainian developers integrate it with existing systems and maintain them. They are ready to solve problems during the operation and ensure the smooth operation of the applications.

6. Cybersecurity

Ukrainian developers actively work in cybersecurity, providing reliable protection of information and data. They know how to protect applications from threats like hacker attacks, viruses, and other malware. And also use modern technologies and methods such as data protection, encryption, and authentication to keep software secure.

Advantages of cooperation with Ukrainian developers

Cooperation with Ukrainian developers today is one of the most advantageous options for companies looking for reliable and experienced IT specialists. Ukraine is one of the world's leaders in the IT services market, thanks to its talented and highly qualified developers capable of fulfilling tasks of any complexity.

• One of the main advantages of working with Ukrainian developers is the high quality of their work. Ukrainian developers have an extensive experience in various fields of IT. They follow the latest trends and use the most advanced technologies. Due to this, they can realize tasks of any complexity, including creating complex websites, mobile applications, software development, and much more.

• In addition, Ukrainian developers are known for their speed and efficiency. They strive to complete their projects on time without compromising the quality of their work. Ukrainian IT companies are also ready to provide flexible working conditions, including working in different time zones, which makes cooperation with them even more convenient and effective.

• One more advantage of cooperation with Ukrainian developers is their price competitiveness. Ukraine offers more favorable conditions for cooperation than other countries while maintaining a high quality of work and professionalism. That allows companies to save on costs without losing quality.

• ‍Flexible cooperation conditions are another advantage of Ukrainian developers. Many of them work remotely, which makes their services more flexible and accessible to clients from all over the world. That reduces the cost of organizing work with remote teams and the time required to find the right specialists. In addition, even remote communication with Ukrainian developers allows you to respond quickly to changes in the project and solve any problems that may arise. Also, communication with Ukrainian developers is in English, which facilitates interaction with customers from around the world.

Summary

Ukraine is one of Europe's largest exporters of IT services, with an excellent knowledge base, great motivation, and great opportunities. Despite the Russian invasion, Ukrainian IT companies continue to provide quality services, support the economy, and maintain a high rate of development and continuous growth.

Cooperation with the Ukrainian company is not only a guarantee of quality work but also support for Ukrainian businesses and Ukraine as a whole. Numerous IT companies survived difficult times and are gaining momentum in their activities due to the trust of international customers and the continuous work of the development team. 

If you regularly think about how to optimize your business operations by speeding them up, increasing their accuracy, reducing related expenses, and eliminating risks associated with the human factor, perhaps the introduction of artificial intelligence will be the best option. 

Specifically, according to the CompTIA IT Industry Outlook 2024 report, today, about 22% of companies are actively seeking to integrate AI into most of their technological solutions and processes, and 45% of companies are considering its implementation. If you are still in the second category, it's time to figure out which programming language will be able to form the basis of your new highly intelligent software tools.

The term “Artificial Intelligence” describes software algorithms and systems that are aimed at solving problems typical to the human mind—such as learning, as well as pattern generation and recognition. Such algorithms are based on colossal amounts of disparate data collected from various sources. Thus, these algorithms form self-learning software solutions capable of analyzing this data and extracting valuable insights from it.

Top 10 Programming Languages for Artificial Intelligence in 2025 

It is worth noting that AI-powered solutions may not be based on any software language—this can be explained by the high resource consumption of the AI algorithms that form them, as well as the lack of high-level functions. At the same time, there are ten languages that are most often used in AI programming. Which one of them? best programming language for AI?

Python 

At the top of our list of programming languages for artificial intelligence is Python. This programming language appeared long before the popularization of AI development. However, thanks to its low entry threshold, open-source code and extensive compatibility, its community quickly grew, and today, Python is considered one of the three most relevant languages worldwide. Beginners can easily find support and useful resources on platforms such as GitHub and Stack Overflow.

As for the tools for artificial intelligence programming, there are a lot of Python-compatible frameworks, libraries, and APIs, aimed at simplifying and speeding up this process. Among them are TensorFlow, PyTorch, Keras, SciPy, NumPy, Scikit-learn and Matplotlib and many others. Using them, developers can create high-performance, reliable, and scalable solutions that can solve even the most complex research problems. This significantly saves time since, instead of creating everything from scratch, you can simply import ready-made tools.

One of the factors that makes Python one of the most popular AI programming languages is its simple and intuitive syntax, which ensures ease of learning.

Python is also known for its versatility, making it a top programming language for artificial intelligence among data analysis scientists and software developers. For example, it is actively used to create neural networks and machine learning algorithms with frameworks like TensorFlow and PyTorch. Additionally, Python is widely applied in data analysis, helping to identify patterns, trends, and correlations.

Best suited for: AI development, data analysis, and machine learning.

Tip: If your project involves resource-intensive computations and requires a detailed analysis of requirements, it is worth considering other AI programming languages.

R

R, being a statistical programming language, is great for data analysis and visualization. Even though developing artificial intelligence and machine learning solutions is not the primary use case for R, it is still adept at handling very large numbers. So, where popular Python may fall short, particularly in Data Science and Data Analysis solutions, R may be a better alternative.

Additional benefits of this developer tool include support for functional programming, vector computing, and OOP, as well as a number of packages specifically created for AI and ML development (for example, gmodels, RODBC, and OneR).

It includes numerous visualization libraries such as Ggplot2, Lattice, and Shiny, which allow for the quick creation of graphs, charts, and other visual representations of data. Thanks to built-in functions like GLM and LM, specialists can perform statistical modeling.

The Caret library enables building, training, and testing machine learning models, as well as optimizing their performance through hyperparameter tuning.

R also provides tools for performance evaluation, including resampling methods and cross-validation, which help developers improve their AI models. Additionally, R supports integration with other programming languages, such as Java and Python, creating a powerful ecosystem for developing various applications.

Best suited for: Statistical analysis, data processing, and visualization in AI projects.

Tip: R is the best programming language for AI in the early stages of research and experimentation. However, its use for deploying machine learning models in real-world products may be limited, as it lacks built-in compilers and efficient memory management. Moreover, it may be challenging to learn for those who do not specialize in data science.

Java

This portable and scalable programming language is actively used in the development of highly intelligent corporate applications, especially for mobile platforms. Thanks to its platform independence, Java programs can run on any operating system where the Java Virtual Machine (JVM) is installed. In addition to compatibility with popular tools for ML programming and AI programming, such as the Deep Java Library for deep learning, OpenNLP for natural language processing, and Neuroph for building neural networks, Java is easy to write and debug program code.

It is worth noting that this language is object-oriented and scalable. It also simplifies data visualization and supports Swing and SWT (Standard Widget Toolkit), which allow developers to create attractive and complex graphics and interfaces.

Java offers a wide range of built-in libraries and modules, such as JUnit and Apache Commons, which accelerate development and help create high-performance applications. For example, Java is actively used in simulations, robotics, and embedded artificial intelligence systems. Additionally, the RTSJ extension enables the development of real-time systems, particularly for robotic solutions.

Support for multithreading makes Java suitable for resource-intensive tasks that require parallel execution. The language scales well, allowing for the creation of both simple and complex applications capable of adapting to growing demands. A large user community provides access to numerous learning materials and valuable resources.

Optimal choice for: Developing scalable, platform-independent AI applications and embedded systems.

Tip: As an artificial intelligence programming language, Java is an excellent choice for large-scale business AI systems where algorithms need to be transformed into stable and reliable software. It ensures high performance, reliability, and compatibility with various devices, making it an efficient choice for building high-performance solutions.

Weakness: Verbose syntax, not specifically designed for AI.

C++

C++ is considered an extremely powerful language for AI programming and can greatly benefit developers when creating games and embedded systems. Like Python, C++ is a mature language, which does not detract from its advantages, such as flexibility and high performance. C++ has several libraries for machine learning and neural networks that help complex algorithms run faster (including MapReduce, mlpack, and MongoDB). In general, many software engineers prefer this language for building projects that require high speed, as it interacts with training modules in a production environment.

Actually, that’s why this low-level language used for AI is primarily a tool for creating complex banking software, cloud systems, and intelligent speech recognition solutions.

C++ plays a key role in developing complex software systems with a high level of abstraction. Its popularity in the field of artificial intelligence is due to its flexibility, performance, and ability to efficiently interact with hardware components.

Although machine learning libraries such as TensorFlow and PyTorch are primarily designed for Python, C++ allows working with them through APIs or bindings. This makes it possible to integrate powerful tools into developed applications without the need to build all algorithms from scratch. Thanks to support for multithreading and parallel computing, C++ is suitable for creating high-performance AI solutions and stands out among other machine learning programming languages.

Developers can manually manage memory allocation, for example, by allocating more resources for computationally intensive tasks, which reduces latency and improves performance. Additionally, using OpenCL and CUDA enables hardware acceleration, enhancing algorithm efficiency by leveraging the computing power of graphics processors.

Best suited for: Developing high-performance AI applications with hardware resource control and memory management.

Weakness: A complex language designed for system programming.

JavaScript

JavaScript is a scripting language used to add interactivity to web pages. Even though it is not as popular as the AI programming languages ​​described above, it can be extremely helpful in implementing solutions for Data Science, one of the most promising areas for using JS.

In addition, JavaScript is the most popular programming language in the world, so you won’t have any problems finding the right AI development tools and frameworks that are compatible with it. The popularity of this coding language for AI can be explained by several factors: reliability, stability, abundance of high-level functions, libraries, and tools, as well as fairly low requirements for other technical skills of developers (it is likely that you will not have any difficulty finding dozens of AI tools that support JS, which will cover the whole scope of work related to your AI project).

Among AI programming languages, JavaScript is particularly useful for developing real-time AI applications, including virtual assistants, chatbots, and recommendation systems. By using APIs, developers can easily integrate artificial intelligence into web applications, expanding their functionality.

Since code execution occurs on the client side, this reduces server load and increases speed, which is especially important for applications requiring instant interaction.

With the emergence of JavaScript frameworks such as TensorFlow.js, Brain.js, and ConvNetJS, it has become possible to directly implement machine learning in web development. These tools allow training and deploying machine learning models directly in the browser, expanding AI capabilities in the web environment.

Best suited for: Developing interactive AI applications in the browser and integrating artificial intelligence into web projects.

Weakness: Not well-suited for mathematical computations and machine learning.

Scala

This machine learning language is concise and expressive, and is often used in big data solutions. Scala is a multi-paradigm language specifically designed to express common programming concepts in a simple, convenient, and type-safe manner.

Scala is commonly used in the development of machine learning algorithms and pattern detection in large data sets, and all this—thanks to the built-in Smile data processing library, extensive compatibility with AI frameworks and libraries (in particular, BigDL, Breeze, DeepLearning.scala, and so on), as well as its object-oriented and scalable nature.

It allows writing clear and efficient code, facilitating the creation of high-performance applications.

One of Scala’s key advantages is its default support for immutability. This means that once declared, objects do not change their state, making them easier to track and manage throughout the entire development lifecycle. Similar to C++, Scala supports multithreading and parallel computing, enabling the creation of AI applications capable of performing multiple tasks simultaneously.

Thanks to its compatibility with Java, developers can leverage tools and libraries built for Java, including Apache Hadoop and Spark, which are essential for processing large volumes of data. This helps analyze information, identify hidden patterns, and make informed decisions.

Scala also features a powerful pattern matching system, making it convenient for natural language processing (NLP). This is particularly important in areas where AI needs to recognize and analyze user input to generate appropriate responses. Scala’s object-oriented approach supports the development of specialized tools, such as for prototyping and research.

Best suited for: Developing scalable AI applications with big data support and real-time information processing.

However, Scala is not the best choice for projects requiring rapid prototyping, such as NLP or machine learning model development in tight timeframes. This is due to a limited selection of AI-focused libraries and frameworks, as well as a longer compilation process, which complicates quick testing and model adjustments.

Julia

Julia is the best programming language for AI powered scientific solutions and technical projects that require statistical data processing. This is a relatively new language with dynamic typing, parallel programming, and JIT compilation that has proven itself well due to its high speed (Julia-based projects are rarely inferior in performance to their C++-based counterparts), flexibility, and a mature ecosystem.

Many of the tools built into this language use polymorphic dispatch, which makes switching between them much faster than in other languages. Julia has gained popularity among AI programming languages for AI developers due to its high performance and rapid prototyping capabilities. It ensures fast data processing, which is critical in the field of artificial intelligence due to the vast amounts of data that need to be analyzed. Moreover, its syntax is clear and similar to Python, making the learning process easier.

Julia supports interaction with other programming languages, such as R, C, and Python. This allows developers to use powerful libraries, including TensorFlow and PyTorch, integrating them into their projects without the need to rewrite code.

With built-in support for parallel computing and distributed computational processes, Julia is capable of effectively handling large datasets and significant computational loads without a noticeable drop in performance.

As an open-source language, Julia provides access to libraries developed by the community, saving time and effort during development. Notable tools include Plots.jl and Gadfly.jl for data visualization, MLJ.jl used for machine and deep learning, as well as Scikitlearn.jl, which is an interface to the Python library scikit-learn.

Best suited for: High-performance AI applications that require fast data processing and easy integration with other languages.

Weakness: Newer language, smaller ecosystem.

Haskell

Haskell is based on the functional programming paradigm and uses key concepts such as pattern matching, higher-order functions, and immutability. This makes it suitable for handling complex data structures in the field of artificial intelligence.

The language has a strong mathematical foundation, which contributes to the development of complex algorithms and even deep learning models. Thanks to lazy evaluation, expressions are only evaluated when needed, allowing for more efficient resource usage.

Haskell's strong type system and type safety control help avoid errors related to incompatible values in variables, improving the reliability and stability of AI applications developed in this language.

Best suited for: Creating AI systems with complex algorithms and a focus on functional programming.

Weakness: Abstract, rarely used in the field of artificial intelligence.

Lisp

Lisp is a high-level functional programming language that is well-suited for artificial intelligence development due to its flexibility and ability to process symbolic expressions. These expressions can contain both simple data types (such as strings or numbers) and complex structures like lists and arrays. This makes Lisp effective in working with large data sets, performing complex computations and manipulations.

The recursive programming mechanism allows for the creation of powerful algorithms for analyzing and processing significant volumes of information. Additionally, automatic garbage collection enables programmers to delegate memory management to the system, focusing on developing core functionality.

Lisp's dynamic typing allows the interpreter to determine variable types during program execution. This provides high flexibility and makes it easy to work with complex data structures, which is important for artificial intelligence tasks.

Lisp is one of the first AI programming languages to support functional programming. Its simple yet powerful syntax structure encourages experimentation and research in AI, especially in areas that require symbolic expression processing.

Best suited for: Scientific research and experimentation in the field of artificial intelligence, particularly for working with symbolic expressions.

Although Lisp was once an innovative language in artificial intelligence, over time it has given way to statistical machine learning and neural networks, which currently dominate the field. However, it remains indispensable for developing expert systems and logical decision-making models based on symbolic reasoning rather than data analysis.

Prolog

Prolog is known for its capabilities in logical programming and symbolic reasoning, making it an effective tool for creating AI solutions, particularly for knowledge representation and solving complex problems.

Thanks to its declarative approach, developers can describe the logic of computations without focusing on the details of execution flow management. This allows for easy specification of rules, relationships, and facts, which is especially useful when creating knowledge bases in artificial intelligence.

The powerful pattern-matching mechanism enables quick analysis of large amounts of information and finding matches based on logical relationships. This makes Prolog a valuable tool in areas such as natural language processing (NLP), helping systems interpret and respond to queries. Additionally, this feature supports the development of various algorithmic solutions.

Best suited for: Symbolic reasoning, knowledge representation, and solving logical problems in AI.

Weakness: Limited usage and a small number of libraries.

What Is the Most Popular Programming Language Used in AI in 2025?

Considering the top programming languages for artificial intelligence, it is worth noting that in 2025, Python remains the most popular. Its popularity is driven by its easy-to-learn syntax and the wide range of libraries and frameworks, such as TensorFlow, PyTorch, and Keras, which significantly simplify the AI development process.

In addition to Python, other languages are also actively used in AI. These include R, known for its powerful statistical analysis and data visualization tools; Java, which offers high performance and scalability; C++, which provides low-level control over hardware; and Julia, which is quickly gaining popularity for its efficiency in numerical computations.

The choice of programming language for AI depends on the specific requirements of the project, available tools, and the personal preferences of the developer.

The Programming Languages That Are Not Recommended for AI

Along with the extensive list of languages for AI development, there are also those that are better used for solving other problems. These are usually niche and low-level languages, such as PHP and Ruby (scripting languages that do not have the needed flexibility and speed to simplify the development process), C (a low-level language that lacks almost all the functions needed in AI development), and Fortran (despite its use for mostly scientific purposes, it has poor functionality).

Ultimately, a seasoned software engineer will be able to cope with any AI development task using any language; another thing is that, given the expected scope of work, the languages that we described in the previous paragraph are much better suited for these tasks.

If you are looking for an experienced team that will launch the digital transformation of your business processes through custom-made AI and ML solutions, feel free to contact us.

Choosing the Best Programming Language for AI Development

From Python to Scala, different programming languages can be used for AI projects. While they share some similarities, each one has unique characteristics that make it optimal for specific tasks.

Here are a few steps that will help you choose the best programming language for your next AI project:

For Your Project

Start by assessing the requirements and needs of your AI project. Pay attention to the structure of the application and the main features it needs to function properly.

Determine whether you are building a web application, a deep learning model, or a robotics project. Different types of projects may require different programming languages.

For example, JavaScript may be suitable for building web applications, such as those related to trading, as it supports many browser features by default. In contrast, C++ would be the optimal choice for robotics development, as it provides low-level hardware integration.

To Learn

Consider your skill level, as programming languages have different learning curves. For instance, Python has a gentle learning curve thanks to its simple and understandable syntax. On the other hand, C++ has a steeper learning curve, meaning it requires more effort to master.

By taking the time to carefully assess these factors, rather than rushing, you will lay a solid foundation for your AI project. Proper planning at the start will help avoid costly problems in the future. Additionally, it will increase your chances of choosing a programming language that best suits both your current needs and long-term goals.